Computational models for disease diagnosis and prognosis applied in a clinical setting can provide unbiased reasoning to assist diagnosis of ambiguous cases, save time by filtering out obvious cases, and help establish degree of disease risk for individual patients. A key component of computational models is identification of nuclei in cell images, on which biomarkers can be measured and related to disease risk. While pathologists have traditionally analyzed nuclei from different cell types according to different criteria, and recent computational findings have uncovered the diagnostic strength of certain cell classes, few automated algorithms exist for categorizing nuclei according to cell-type.